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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2014  >  43(5): 724-729

LIU Yao, WANG Rui-jin, LIU Qiao, QIN Zhi-guang. Community Detecting and Analyzing in Dynamic Social Networks[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(5): 724-729. doi: 10.3969/j.issn.1001-0548.2014.05.016
Citation: LIU Yao, WANG Rui-jin, LIU Qiao, QIN Zhi-guang. Community Detecting and Analyzing in Dynamic Social Networks[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(5): 724-729. doi: 10.3969/j.issn.1001-0548.2014.05.016
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Community Detecting and Analyzing in Dynamic Social Networks

doi: 10.3969/j.issn.1001-0548.2014.05.016
  • 1.

    School of Computer Science and Engineering,University of Electronic Science and Technology of China Chengdu 610054

  • Received Date: 2013-03-20
  • Rev Recd Date: 2014-03-19
  • Publish Date: 2014-10-15
  • Real networks such as e-mail, co-author and peer-to-peer networks can be modeled as graphs. Community mining on graphs has attracted more and more attentions in recent years. It not only can help to identify the overall structures of networks, but also can help to discover the latent rules of community evolution. Community mining on dynamic graphs has not been studied thoroughly, although that on static graphs has been exploited extensively. Based on time-sequence, the community mining including community detection and analysis on dynamic graphs is researched in this paper. And a two-step model is presented to discover the dynamic community structure. The effectiveness and efficiency of the model are validated by experiments on real networks. Results show that the model has a good trade-off between the effectiveness and efficiency in discovering communities.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Community Detecting and Analyzing in Dynamic Social Networks

doi: 10.3969/j.issn.1001-0548.2014.05.016
  • 1. School of Computer Science and Engineering,University of Electronic Science and Technology of China Chengdu 610054
  • Received Date: 2013-03-20
  • Rev Recd Date: 2014-03-19
  • Publish Date: 2014-10-15

Abstract: Real networks such as e-mail, co-author and peer-to-peer networks can be modeled as graphs. Community mining on graphs has attracted more and more attentions in recent years. It not only can help to identify the overall structures of networks, but also can help to discover the latent rules of community evolution. Community mining on dynamic graphs has not been studied thoroughly, although that on static graphs has been exploited extensively. Based on time-sequence, the community mining including community detection and analysis on dynamic graphs is researched in this paper. And a two-step model is presented to discover the dynamic community structure. The effectiveness and efficiency of the model are validated by experiments on real networks. Results show that the model has a good trade-off between the effectiveness and efficiency in discovering communities.

LIU Yao, WANG Rui-jin, LIU Qiao, QIN Zhi-guang. Community Detecting and Analyzing in Dynamic Social Networks[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(5): 724-729. doi: 10.3969/j.issn.1001-0548.2014.05.016
Citation: LIU Yao, WANG Rui-jin, LIU Qiao, QIN Zhi-guang. Community Detecting and Analyzing in Dynamic Social Networks[J]. Journal of University of Electronic Science and Technology of China, 2014, 43(5): 724-729. doi: 10.3969/j.issn.1001-0548.2014.05.016
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